Selected work

Representative Papers

An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing

Oct 04, 2026

This study addresses the limitations of global evaluation metrics, which obscure error sources and complicate the assessment of benchmark saturation, by proposing an information-theoretic fine-grained evaluation framework. It introduces the Context Tree Weighting (CTW) algorithm as an operational coordinate to quantify local predictability at the entropy-band level, effectively distinguishing causal from irreducible uncertainty. Experiments demonstrate that modern models achieve significant yet non-uniform gains in high-entropy regions. Furthermore, the proposed method precisely identifies residual predictive structures and noise-sensitive areas, revealing inherent benchmark limitations. Ultimately, this framework provides a reliable analytical tool for diagnosing the performance of large language models.

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Learn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO

Sep 29, 2026

This study addresses the high computational overhead caused by multi-trajectory sampling and repeated evaluation in Group Relative Policy Optimization (GRPO), as well as the reduced learning efficiency due to low-quality trajectories. To this end, we propose FastRL, a plug-and-play reinforcement learning framework. It introduces a novel differential-aware advantage pruning algorithm that eliminates homogeneous trajectories to maximize gradient diversity, alongside an adaptive rolling sampling mechanism that dynamically adjusts the sampling scale to effectively balance exploration and efficiency. The framework is compatible with GRPO variants such as DAPO and GSPO. Experiments demonstrate that FastRL achieves an average 2.07× training speedup and a 1.64% improvement in visual reasoning accuracy on benchmarks including Geometry3K. The source code has been made publicly available.

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Recent publications

Latest Papers

An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing

Oct 04, 2026

This study addresses the limitations of global evaluation metrics, which obscure error sources and complicate the assessment of benchmark saturation, by proposing an information-theoretic fine-grained evaluation framework. It introduces the Context Tree Weighting (CTW) algorithm as an operational coordinate to quantify local predictability at the entropy-band level, effectively distinguishing causal from irreducible uncertainty. Experiments demonstrate that modern models achieve significant yet non-uniform gains in high-entropy regions. Furthermore, the proposed method precisely identifies residual predictive structures and noise-sensitive areas, revealing inherent benchmark limitations. Ultimately, this framework provides a reliable analytical tool for diagnosing the performance of large language models.

0 citationsRead paper

Learn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO

Sep 29, 2026

This study addresses the high computational overhead caused by multi-trajectory sampling and repeated evaluation in Group Relative Policy Optimization (GRPO), as well as the reduced learning efficiency due to low-quality trajectories. To this end, we propose FastRL, a plug-and-play reinforcement learning framework. It introduces a novel differential-aware advantage pruning algorithm that eliminates homogeneous trajectories to maximize gradient diversity, alongside an adaptive rolling sampling mechanism that dynamically adjusts the sampling scale to effectively balance exploration and efficiency. The framework is compatible with GRPO variants such as DAPO and GSPO. Experiments demonstrate that FastRL achieves an average 2.07× training speedup and a 1.64% improvement in visual reasoning accuracy on benchmarks including Geometry3K. The source code has been made publicly available.

0 citationsRead paper